Replication 1

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.42      0.641       -2.22  0.0574
## 2 mortality$`Grp 1 Dead`  0.00446   0.00338      1.32  0.224
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid  .hat .sigma
##               <dbl>            <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>
##  1            0                   24  -1.31    0.575  1.31  0.274  1.02 
##  2           -3                   13  -1.36    0.605 -1.64  0.303  0.910
##  3           -2                   27  -1.30    0.566 -0.699 0.266  1.13 
##  4           -1.30               179  -0.624   0.353 -0.677 0.104  1.14 
##  5           -1                  295  -0.107   0.575 -0.893 0.274  1.10 
##  6           -0.602              259  -0.267   0.483 -0.335 0.194  1.17 
##  7           -0.301              195  -0.553   0.367  0.252 0.112  1.17 
##  8            0                  294  -0.111   0.572  0.111 0.271  1.17 
##  9            0.398              144  -0.780   0.351  1.18  0.102  1.08 
## 10            0.699              165  -0.686   0.348  1.39  0.100  1.04 
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.179        0.0760  1.10      1.74   0.224     2  -14.0  34.0  34.9
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##          1          2          3          4          5          6 
## -1.3144841 -1.3634981 -1.3011166 -0.6238326 -0.1069579 -0.2673673 
##          7          8          9         10 
## -0.5525395 -0.1114137 -0.7797861 -0.6862140
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.42      0.641       -2.22  0.0574
## 2 mortality$`Grp 1 Dead`  0.00446   0.00338      1.32  0.224
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 1 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -5.176e-05   2.100e-08   2.100e-08   2.100e-08   6.247e-05  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)
## (Intercept)              -68.278  39632.039  -0.002    0.999
## mortality$`Grp 1 Dead`     3.681   1956.504   0.002    0.998
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017e+00  on 9  degrees of freedom
## Residual deviance: 6.5811e-09  on 8  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
##            1            2            3            4            5 
## 1.000000e+00 1.339598e-09 1.000000e+00 1.000000e+00 1.000000e+00 
##            6            7            8            9           10 
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)              -68.3     39632.  -0.00172   0.999
## 2 mortality$`Grp 1 Dead`     3.68     1957.   0.00188   0.998
##               Dose       SE
## p = 0.5: 113927309 2919.175
##    Concentration Mortality     Fitted    Predicted
## 1        0.00000        24 -1.3144841 1.000000e+00
## 2       -3.00000        13 -1.3634981 1.339598e-09
## 3       -2.00000        27 -1.3011166 1.000000e+00
## 4       -1.30103       179 -0.6238326 1.000000e+00
## 5       -1.00000       295 -0.1069579 1.000000e+00
## 6       -0.60206       259 -0.2673673 1.000000e+00
## 7       -0.30103       195 -0.5525395 1.000000e+00
## 8        0.00000       294 -0.1114137 1.000000e+00
## 9        0.39794       144 -0.7797861 1.000000e+00
## 10       0.69897       165 -0.6862140 1.000000e+00

### Replication 2

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.60      0.744       -2.15  0.0640
## 2 mortality$`Grp 2 Dead`  0.00341   0.00253      1.35  0.215
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid  .hat .sigma
##               <dbl>            <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>
##  1            0                  115 -1.20     0.504  1.20  0.213  1.05 
##  2           -3                  105 -1.24     0.523 -1.76  0.229  0.891
##  3           -2                   79 -1.33     0.574 -0.672 0.275  1.13 
##  4           -1.30               151 -1.08     0.442 -0.219 0.164  1.17 
##  5           -1                  426 -0.144    0.545 -0.856 0.248  1.11 
##  6           -0.602              435 -0.114    0.562 -0.488 0.264  1.15 
##  7           -0.301              441 -0.0931   0.575 -0.208 0.276  1.17 
##  8            0                  336 -0.451    0.396  0.451 0.131  1.16 
##  9            0.398              260 -0.710    0.346  1.11  0.100  1.08 
## 10            0.699              251 -0.741    0.347  1.44  0.100  1.02 
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.185        0.0828  1.09      1.81   0.215     2  -14.0  33.9  34.8
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##           1           2           3           4           5           6 
## -1.20485325 -1.23895485 -1.32761902 -1.08208747 -0.14429335 -0.11360190 
##           7           8           9          10 
## -0.09314094 -0.45120779 -0.71037998 -0.74107142
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.60      0.744       -2.15  0.0640
## 2 mortality$`Grp 2 Dead`  0.00341   0.00253      1.35  0.215
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 2 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.63995   0.01658   0.09181   0.38937   1.00505  
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)
## (Intercept)            -1.47261    3.81599  -0.386    0.700
## mortality$`Grp 2 Dead`  0.02396    0.03320   0.722    0.471
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017  on 9  degrees of freedom
## Residual deviance: 4.4526  on 8  degrees of freedom
## AIC: 8.4526
## 
## Number of Fisher Scoring iterations: 8
##         1         2         3         4         5         6         7 
## 0.7828483 0.7393864 0.6034647 0.8951784 0.9998388 0.9998701 0.9998875 
##         8         9        10 
## 0.9986094 0.9914732 0.9894432
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)             -1.47      3.82      -0.386   0.700
## 2 mortality$`Grp 2 Dead`   0.0240    0.0332     0.722   0.471
##              Dose       SE
## p = 0.5: 61.47117 82.61537
##    Concentration Mortality      Fitted Predicted
## 1        0.00000       115 -1.20485325 0.7828483
## 2       -3.00000       105 -1.23895485 0.7393864
## 3       -2.00000        79 -1.32761902 0.6034647
## 4       -1.30103       151 -1.08208747 0.8951784
## 5       -1.00000       426 -0.14429335 0.9998388
## 6       -0.60206       435 -0.11360190 0.9998701
## 7       -0.30103       441 -0.09314094 0.9998875
## 8        0.00000       336 -0.45120779 0.9986094
## 9        0.39794       260 -0.71037998 0.9914732
## 10       0.69897       251 -0.74107142 0.9894432

### Replication 3

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -2.02      0.976       -2.07  0.0724
## 2 mortality$`Grp 3 Dead`  0.00381   0.00266      1.43  0.190
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~ .fitted .se.fit  .resid  .hat .sigma
##               <dbl>            <dbl>   <dbl>   <dbl>   <dbl> <dbl>  <dbl>
##  1            0                  161 -1.41     0.594  1.41   0.302  0.965
##  2           -3                  300 -0.875    0.361 -2.12   0.111  0.781
##  3           -2                  221 -1.18     0.472 -0.823  0.191  1.10 
##  4           -1.30               137 -1.50     0.647  0.196  0.358  1.15 
##  5           -1                  425 -0.399    0.405 -0.601  0.141  1.13 
##  6           -0.602              415 -0.437    0.392 -0.165  0.131  1.15 
##  7           -0.301              458 -0.273    0.459 -0.0279 0.180  1.16 
##  8            0                  418 -0.426    0.396  0.426  0.134  1.14 
##  9            0.398              350 -0.685    0.342  1.08   0.100  1.07 
## 10            0.699              547  0.0661   0.641  0.633  0.352  1.12 
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.204         0.104  1.08      2.05   0.190     2  -13.9  33.7  34.6
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##           1           2           3           4           5           6 
## -1.40520816 -0.87538535 -1.17650767 -1.49668836 -0.39892599 -0.43704274 
##           7           8           9          10 
## -0.27314072 -0.42560772 -0.68480161  0.06609834
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -2.02      0.976       -2.07  0.0724
## 2 mortality$`Grp 3 Dead`  0.00381   0.00266      1.43  0.190
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 3 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1168   0.3862   0.4043   0.5067   0.5904  
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)
## (Intercept)            1.264622   2.720932   0.465    0.642
## mortality$`Grp 3 Dead` 0.002877   0.008213   0.350    0.726
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017  on 9  degrees of freedom
## Residual deviance: 6.3774  on 8  degrees of freedom
## AIC: 10.377
## 
## Number of Fisher Scoring iterations: 5
##         1         2         3         4         5         6         7 
## 0.8491421 0.8935808 0.8699519 0.8400808 0.9232629 0.9211993 0.9297257 
##         8         9        10 
## 0.9218236 0.9065078 0.9447251
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)             1.26      2.72        0.465   0.642
## 2 mortality$`Grp 3 Dead`  0.00288   0.00821     0.350   0.726
##               Dose     SE
## p = 0.5: -439.4975 2156.9
##    Concentration Mortality      Fitted Predicted
## 1        0.00000       161 -1.40520816 0.8491421
## 2       -3.00000       300 -0.87538535 0.8935808
## 3       -2.00000       221 -1.17650767 0.8699519
## 4       -1.30103       137 -1.49668836 0.8400808
## 5       -1.00000       425 -0.39892599 0.9232629
## 6       -0.60206       415 -0.43704274 0.9211993
## 7       -0.30103       458 -0.27314072 0.9297257
## 8        0.00000       418 -0.42560772 0.9218236
## 9        0.39794       350 -0.68480161 0.9065078
## 10       0.69897       547  0.06609834 0.9447251

### Replication 4

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.83      0.561       -3.26  0.0116
## 2 mortality$`Grp 4 Dead`  0.00311   0.00133      2.34  0.0473
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid  .hat .sigma
##               <dbl>            <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>
##  1            0                   58 -1.65     0.497  1.65  0.284  0.673
##  2           -3                  100 -1.52     0.454 -1.48  0.236  0.764
##  3           -2                   77 -1.59     0.477 -0.411 0.262  0.981
##  4           -1.30               125 -1.44     0.429  0.139 0.211  0.996
##  5           -1                  486 -0.317    0.340 -0.683 0.132  0.958
##  6           -0.602              598  0.0311   0.433 -0.633 0.215  0.960
##  7           -0.301              527 -0.190    0.370 -0.111 0.157  0.996
##  8            0                  524 -0.199    0.367  0.199 0.155  0.994
##  9            0.398              555 -0.103    0.393  0.501 0.177  0.975
## 10            0.699              545 -0.134    0.384  0.833 0.170  0.936
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.407         0.333 0.933      5.49  0.0473     2  -12.4  30.8  31.7
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##           1           2           3           4           5           6 
## -1.64851954 -1.51788093 -1.58942112 -1.44011986 -0.31724997  0.03111964 
##           7           8           9          10 
## -0.18972181 -0.19905314 -0.10262941 -0.13373384
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.83      0.561       -3.26  0.0116
## 2 mortality$`Grp 4 Dead`  0.00311   0.00133      2.34  0.0473
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 4 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.73192   0.09545   0.10795   0.51351   0.83912  
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)
## (Intercept)            0.331796   1.744215   0.190    0.849
## mortality$`Grp 4 Dead` 0.009154   0.012421   0.737    0.461
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017  on 9  degrees of freedom
## Residual deviance: 4.7871  on 8  degrees of freedom
## AIC: 8.7871
## 
## Number of Fisher Scoring iterations: 7
##         1         2         3         4         5         6         7 
## 0.7032346 0.7768202 0.7382091 0.8139813 0.9916806 0.9969999 0.9942691 
##         8         9        10 
## 0.9941105 0.9955592 0.9951355
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)             0.332      1.74       0.190   0.849
## 2 mortality$`Grp 4 Dead`  0.00915    0.0124     0.737   0.461
##               Dose       SE
## p = 0.5: -36.24467 229.8498
##    Concentration Mortality      Fitted Predicted
## 1        0.00000        58 -1.64851954 0.7032346
## 2       -3.00000       100 -1.51788093 0.7768202
## 3       -2.00000        77 -1.58942112 0.7382091
## 4       -1.30103       125 -1.44011986 0.8139813
## 5       -1.00000       486 -0.31724997 0.9916806
## 6       -0.60206       598  0.03111964 0.9969999
## 7       -0.30103       527 -0.18972181 0.9942691
## 8        0.00000       524 -0.19905314 0.9941105
## 9        0.39794       555 -0.10262941 0.9955592
## 10       0.69897       545 -0.13373384 0.9951355

### Replication 5

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -2.07      0.581       -3.55 0.00746
## 2 mortality$`Grp 5 Dead`  0.00389   0.00146      2.66 0.0289
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid  .hat .sigma
##               <dbl>            <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>
##  1            0                  260 -1.05     0.307  1.05  0.121  0.843
##  2           -3                   46 -1.89     0.523 -1.11  0.351  0.786
##  3           -2                  108 -1.65     0.449 -0.355 0.259  0.931
##  4           -1.30               154 -1.47     0.398  0.165 0.204  0.941
##  5           -1                  409 -0.473    0.293 -0.527 0.110  0.920
##  6           -0.602              548  0.0681   0.405 -0.670 0.210  0.900
##  7           -0.301              638  0.419    0.508 -0.720 0.332  0.883
##  8            0                  499 -0.123    0.356  0.123 0.163  0.942
##  9            0.398              483 -0.185    0.342  0.583 0.150  0.913
## 10            0.699              335 -0.761    0.280  1.46  0.100  0.743
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.469         0.403 0.883      7.07  0.0289     2  -11.8  29.7  30.6
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##           1           2           3           4           5           6 
## -1.05339872 -1.88672863 -1.64529660 -1.46616961 -0.47318303  0.06809201 
##           7           8           9          10 
##  0.41855786 -0.12271718 -0.18502222 -0.76134384
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -2.07      0.581       -3.55 0.00746
## 2 mortality$`Grp 5 Dead`  0.00389   0.00146      2.66 0.0289
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 5 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.714e-05   2.100e-08   2.100e-08   2.100e-08   3.344e-05  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)
## (Intercept)              -53.645  58245.152  -0.001    0.999
## mortality$`Grp 5 Dead`     0.694    656.473   0.001    0.999
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017e+00  on 9  degrees of freedom
## Residual deviance: 1.8550e-09  on 8  degrees of freedom
## AIC: 4
## 
## Number of Fisher Scoring iterations: 25
##            1            2            3            4            5 
## 1.000000e+00 3.683763e-10 1.000000e+00 1.000000e+00 1.000000e+00 
##            6            7            8            9           10 
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)             -53.6      58245. -0.000921   0.999
## 2 mortality$`Grp 5 Dead`    0.694      656.  0.00106    0.999
##              Dose       SE
## p = 0.5: 77.30064 29268.99
##    Concentration Mortality      Fitted    Predicted
## 1        0.00000       260 -1.05339872 1.000000e+00
## 2       -3.00000        46 -1.88672863 3.683763e-10
## 3       -2.00000       108 -1.64529660 1.000000e+00
## 4       -1.30103       154 -1.46616961 1.000000e+00
## 5       -1.00000       409 -0.47318303 1.000000e+00
## 6       -0.60206       548  0.06809201 1.000000e+00
## 7       -0.30103       638  0.41855786 1.000000e+00
## 8        0.00000       499 -0.12271718 1.000000e+00
## 9        0.39794       483 -0.18502222 1.000000e+00
## 10       0.69897       335 -0.76134384 1.000000e+00

### Replication 6

## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.57      0.578       -2.72  0.0263
## 2 mortality$`Grp 6 Dead`  0.00254   0.00141      1.80  0.110
## # A tibble: 10 x 9
##    mortality..Log.~ mortality..Grp.~  .fitted .se.fit   .resid  .hat .sigma
##               <dbl>            <dbl>    <dbl>   <dbl>    <dbl> <dbl>  <dbl>
##  1            0                   55 -1.43e+0   0.515  1.43e+0 0.254  0.896
##  2           -3                  114 -1.28e+0   0.453 -1.72e+0 0.197  0.819
##  3           -2                   61 -1.42e+0   0.509 -5.83e-1 0.248  1.06 
##  4           -1.30               126 -1.25e+0   0.442 -4.97e-2 0.187  1.09 
##  5           -1                  334 -7.23e-1   0.323 -2.77e-1 0.100  1.09 
##  6           -0.602              653  8.81e-2   0.550 -6.90e-1 0.289  1.05 
##  7           -0.301              616 -5.95e-3   0.508 -2.95e-1 0.247  1.09 
##  8            0                  618 -8.69e-4   0.510  8.69e-4 0.249  1.09 
##  9            0.398              346 -6.92e-1   0.324  1.09e+0 0.100  1.00 
## 10            0.699              464 -3.92e-1   0.369  1.09e+0 0.130  1.000
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>  <dbl> <dbl> <dbl>
## 1     0.288         0.199  1.02      3.23   0.110     2  -13.3  32.6  33.5
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
##            1            2            3            4            5 
## -1.431755693 -1.281804875 -1.416506457 -1.251306403 -0.722666232 
##            6            7            8            9           10 
##  0.088084801 -0.005952153 -0.000869074 -0.692167760 -0.392266124
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)            -1.57      0.578       -2.72  0.0263
## 2 mortality$`Grp 6 Dead`  0.00254   0.00141      1.80  0.110
## 
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~ 
##     mortality$`Grp 6 Dead`, family = binomial(link = "logit"))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.82409   0.09227   0.21940   0.53611   0.79681  
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)
## (Intercept)            0.547193   1.682679   0.325    0.745
## mortality$`Grp 6 Dead` 0.007951   0.010176   0.781    0.435
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6.5017  on 9  degrees of freedom
## Residual deviance: 5.1614  on 8  degrees of freedom
## AIC: 9.1614
## 
## Number of Fisher Scoring iterations: 7
##         1         2         3         4         5         6         7 
## 0.7280014 0.8105555 0.7373447 0.8247746 0.9609424 0.9967933 0.9957011 
##         8         9        10 
## 0.9957687 0.9643701 0.9857483
## # A tibble: 2 x 5
##   term                   estimate std.error statistic p.value
##   <chr>                     <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)             0.547      1.68       0.325   0.745
## 2 mortality$`Grp 6 Dead`  0.00795    0.0102     0.781   0.435
##               Dose       SE
## p = 0.5: -68.81935 283.2402
##    Concentration Mortality       Fitted Predicted
## 1        0.00000        55 -1.431755693 0.7280014
## 2       -3.00000       114 -1.281804875 0.8105555
## 3       -2.00000        61 -1.416506457 0.7373447
## 4       -1.30103       126 -1.251306403 0.8247746
## 5       -1.00000       334 -0.722666232 0.9609424
## 6       -0.60206       653  0.088084801 0.9967933
## 7       -0.30103       616 -0.005952153 0.9957011
## 8        0.00000       618 -0.000869074 0.9957687
## 9        0.39794       346 -0.692167760 0.9643701
## 10       0.69897       464 -0.392266124 0.9857483